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Model: anujjamwal/OpenMath-Nemotron-1.5B-PruneAware
Source: Original Platform

README.md

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+---
+base_model: anujjamwal/OpenMath-Nemotron-1.5B-PruneAware
+library_name: transformers
+model_name: OpenMath-Nemotron-1.5B-PruneAware
+tags:
+- generated_from_trainer
+- sft
+- trl
+- custom_generate
+licence: license
+datasets:
+- anujjamwal/OpenMathReasoning-Sampled-Hierarchical-Cot
+---
+
+# Model Card for OpenMath-Nemotron-1.5B-PruneAware
+
+This model implements [Cognitive Compression](https://github.com/anujjamwal/cognitive-compression) an approach to produce hierarchical
+structured chain of thought that can be actively pruned at inference time while maintaining the solution quality.
+Tradition Chain-of-Thought is append-onl; a token once generated remains in context for ever. Context compression introduces hierarchical
+reasoning where reasoning is broken into subproblems. Once the subproblem is solved, its full chain of thought can be discarded and
+replaced with the **summary and solution** dramatically reducing the context window pressure.
+
+This model is a fine-tuned version of [anujjamwal/OpenMath-Nemotron-1.5B-PruneAware](https://huggingface.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAware).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="anujjamwal/OpenMath-Nemotron-1.5B-PruneAware", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+ 
+
+
+
+This model was trained with SFT.
+
+### Framework versions
+
+- TRL: 0.29.0
+- Transformers: 5.0.0
+- Pytorch: 2.10.0+cu128
+- Datasets: 4.0.0
+- Tokenizers: 0.22.2
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{jamwal2026cognitivecompression,
+  title   = {{Cognitive Compression: Hierarchical Chain of Thought for Efficient LLM Reasoning}},
+  author  = {Jamwal, Anuj},
+  url     = {huggingface.co/anujjamwal/OpenMath-Nemotron-1.5B-PruneAware},
+  year    = {2026},
+  note    = {CS224N Winter '26 Final Project: Stanford University}
+}
+```